Outcome prediction by the knowledge bank approach in <scp>acute myeloid leukemia</scp> patients undergoing allogeneic stem cell transplantation
Authors: Lisa Herrmann, Lara Bischof, Donata Backhaus, Dominic Brauer, Georg‐Nikolaus Franke, Vladan Vucinic, Uwe Platzbecker, Sebastian Schwind, Madlen Jentzsch
Journal: American Journal of Hematology (2022)

Acute myeloid leukemia (AML) is a disease resulting from different molecular aberrations which give rise to heterogeneous biological characteristics and patient outcomes.1 Most patients are older than 60 years at diagnosis and often present with clinically relevant comorbidities. Therefore, given the associated risk of therapy-related morbidity and mortality, a profound and individualized risk assessment is essential to decide on the optimal treatment approach, including the choice to perform an allogeneic stem cell transplantation (HSCT) in first complete remission (CR). Today, the risk stratification is largely based on the presence of certain genetic abnormalities, such as those assessed by the European LeukemiaNet (ELN) 2017.1 In a more recent approach, Gerstung et al.2 developed a knowledge-bank (KB)-based algorithm, predicting individual patients' outcomes based on genetic, clinical, and demographic features, also anticipating the effects of a planned allogeneic HSCT. As of today, two studies evaluated the KB-based algorithm in the routine clinical practice by comparing outcome predictions to that by the ELN2017 risk stratification. The first included 155 AML patients and showed the superiority of the KB approach for a 3-year overall survival (OS) prediction, including discrimination between non-remission and relapse death.3 Another study with 1612 de novo AML patients showed the KB algorithm to perform well in predicting 3-year OS and non-remission death, but inferior in predicting relapse death and death in first complete remission.4 Remarkably, the ability of the KB approach to predict outcomes in individuals that received an allogeneic HSCT has not been validated yet. Only 70 patients in first CR (n = 54) or after relapse (n = 16) in the first study and none in the second study had an allogeneic HSCT.3, 4 However, in order to inform treatment decision toward or against an HSCT based on this approach, it is important to validate the algorithm in AML patients treated with HSCT. Here, we analyzed a cohort of 546 newly diagnosed AML patients (median age at diagnosis 62.2; range, 20.7–76.5 years) who received an allogeneic HSCT after reduced intensity or non-myeloablative conditioning at our institution. Most patients were transplanted in first CR/CR with incomplete recovery (CRi) (n = 327, 60%), 17% were transplanted in second CR/CRi (n = 91) and 23% without reaching CR/CRi (n = 126). Additional details on the applied therapies, and further patients' characteristics are given in the Supporting Information Methods S1 and Table S1. Patients were retrospectively screened for clinical and genetic parameters included in the KB. The mutation status of 54 genes included in the TruSight Myeloid Sequencing Panel (Illumina) at diagnosis was performed in patients with adequate pre-treatment material available. Subsequently, the mutation status of 17 genes included in the KB algorithm was not available in our cohort (see Supporting Information Methods S1). The KB estimates were calculated for each individual with the actually performed transplant strategy and compared to the observed outcomes using receiver operating characteristics (ROC) curves. Additionally, the measurable residual disease (MRD) status was evaluated prior to allogeneic HSCT as previously published5 and described in the Supporting Information Methods S1. In our cohort, the KB approach had an area under the curve (AUC) to predict OS at 1 and 3 year of AUCKB = 0.68 [95% CI 0.61–0.72] and AUCKB = 0.69 [95% CI 0.62–0.72], respectively. Both OS predictions were not significantly different to those predicted by the ELN2017 risk stratification (1-year OS p = .33, Figure S1 and 3-year OS p = .23, Figure 1A). Although sub-analyses were restricted by lower patient numbers, comparable results were obtained in separate analyses of patients transplanted in first CR/CRi (see Figure S2) as well as the distinct conditioning intensities (non-myeloablative and reduced-intensity, Figure S3 and S4, respectively). Importantly, in the study by Bill et al.,4 in which no patient received an allogeneic HSCT, the AUCKB was 0.80. A second large study that used a combination of the KB, ELN2017, and MRD to inform the decision for an allogeneic HSCT in first remission in which approximately half of the patients underwent allogeneic HSCT also resulted in a lower AUCKB of 0.69.6 Altogether, the data indicate a reduced capability of the KB approach to estimate individual OS in patients undergoing allogeneic HSCT—most likely due to confounders introduced through donor selection, comorbidities, and conditioning regimens, which are not reflected in the KB algorithm today. Next, we performed a multivariate analysis including variables not reflected in the KB approach (Supporting Information Methods S1). Here, the KB prediction for 3-year OS as a continuous parameter remained an independent prognostic factor for OS after adjustment for the MRD-corrected remission status prior to allogeneic HSCT (Table S2A). When comparing the Akaike information criterion (AIC) to a second model containing the ELN2017 genetic risk together with the remission status (Table S2B), the one containing the KB showed a lower AIC and was identified as the statistically preferred model. To further visualize the prognostic value of the KB approach, we then introduced arbitrary cut-offs according to the KB value for OS similar to Fenwarth et al.,6 and observed a clear separation of OS curves according to a KB value of <20, 20–39, and ≥40 with higher values indicating a higher likelihood for OS (p < .001, Figure 1B). The ability to identify distinct risk groups was also seen in separate analyses for patients transplanted in a first CR/CRi (p < .001, Figure S2C). However, the “three-groups” KB model did not perform superior to the prognostication by the ELN2017 genetic risk (AUC comparison, p = .54). Furthermore, we analyzed additional endpoints for which the KB algorithm provided outcome prediction. Similar to previously published data,3, 4 the KB approach had the highest probability to predict death without previous achievement of a CR within 3 years after diagnosis (AUCKB = 0.75, 95% CI 0.57–0.92, Figure 1C, black curve). In contrast, and again congruent with published results in patients receiving chemotherapy,3, 4 outcome prediction for death in first CR (AUCKB = 0.61, 95% CI 0.55–0.68, Figure 1C, red curve) and death after relapse (AUCKB = 0.63, 95% CI 0.58–0.69, Figure 1C, blue curve) were limited. Finally, we observed good outcome prediction for “being alive after relapse” (AUCKB = 0.77, 95% CI 0.71–0.84, Figure 1D, blue curve) as well as “being alive in first CR” (AUCKB = 0.69, 95% CI 0.64–0.74, Figure 1D, red curve). Our data also imply that a “salvage” allogeneic HSCT in our cohort did not improve outcomes for most patients with primary induction failure (“death without CR”), which is reflected by a relatively good KB prediction for this endpoint. The potential of the KB approach to predict survival with or without relapse was better than the ability to predict death, which most likely is caused by difficulties to estimate treatment-related complications after allogeneic HSCT, including infections and graft-versus-host disease. However, all ROC curves derived from our analyses seemed to show inferior outcome predictions compared to those of previous studies of patients receiving chemotherapy consolidation.3, 4 In conclusion, our study is the first to validate the KB algorithm in AML patients undergoing allogeneic HSCT. While the KB approach allows outcome prediction in these patients, the predictive power seems to be restricted compared to patients receiving chemotherapy consolidation, as donor selection, or conditioning regimens are not integrated into the score and introduce significant confounders, especially for treatment-related mortality. Subsequently, future versions of the KB that also consider HSCT-associated factors might further improve outcome prediction of this promising and important clinical tool. The study was conducted according to the guidelines of the Declaration of Helsinki. Data analyses were approved by the Institutional Review Board of the University Hospital Leipzig. Informed consent was obtained from all subjects involved in the study. Lisa Herrmann, Madlen Jentzsch, and Sebastian Schwind contributed to the design and analysis of this study. Lisa Herrmann, Lara Bischof, Donata Backhaus, Dominic Brauer, and Madlen Jentzsch carried out the clinical and laboratory-based research. Madlen Jentzsch and Sebastian Schwind performed statistical analyses. Georg-Nikolaus Franke, Vladan Vucinic, Uwe Platzbecker, and Sebastian Schwind provided administrative support. All authors contributed to writing the manuscript and agreed on the final version. This study was supported by the Verein Zusammen gegen den Krebs e.V (Sebastian Schwind), the Deutsche Gesellschaft für Innere Medizin (Clinician Scientist Program [Madlen Jentzsch]), and an intramural scholarship of the Leipzig University (Lara Bischof). The authors would like to thank Christel Müller, Daniela Bretschneider, Evelin Hennig, Sabine Leiblein, Martina Pleß, Ulrike Bergmann, Janet Bogardt, Annette Jilo, and Dagmar Cron for their help in determine cytogenetic, morphologic, and immunological analyses; Julia Schulz, Christine Günther, Scarlett Schwabe, Ines Kovacs, and Kathrin Wildenberger for their help in sample processing; and Karoline Goldmann, Marius Bill, and Juliane Grimm for their help in molecular analyses. The authors would also like to express their sincere thanks and gratitude to Dietger Niederwieser for his supervision and support during the last decade. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Appendix S1 Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.